Markov kernel

In probability theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes plays the role that the transition matrix does in the theory of Markov processes with a finite state space.

In probability theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes plays the role that the transition matrix does in the theory of Markov processes with a finite state space. Take X=Y=\Z , and \mathcal A = \mathcal B = \mathcal P(\Z) (the power set of \Z ). Then a Markov kernel is fully determined by the probability it assigns to singletons \{m\},\, m \in Y = \Z for each n \in X = \Z : \kappa(B|n )=\sum_{m \in B}\kappa(\{m\}|n), \qquad \forall n \in \mathbb{Z}, \, \forall B \in \mathcal B . Now the random walk \kappa that goes to the right with probability p and to the left with probability 1 - p is defined by \kappa(\{m\}|n)= p \delta_{m, n + 1}+ (1-p) \delta_{m, n - 1}, \quad \forall n,m \in \Z where \delta is the Kronecker delta. The transition probabilities P(m|n) = \kappa(\{m\}|n) for the random walk are equivalent to the Markov kernel. ===General Markov processes with countable state space=== More generally take X and Y both countable and \mathcal A = \mathcal P(X),\ \mathcal B = \mathcal P(Y) . Again a Markov kernel is defined by the probability it assigns to singleton sets for each i \in X \kappa(B|i)=\sum_{j \in B}\kappa(\{j\}|i), \qquad \forall i \in X, \, \forall B \in \mathcal B , We define a Markov process by defining a transition probability P(j|i) = K_{ji} where the numbers K_{ji} define a (countable) stochastic matrix (K_{ji}) i.e. \begin{align} K_{ji} &\ge 0, \qquad &\forall (j,i) \in Y\times X, \\ \sum_{j \in Y}K_{ji}&=1, \qquad &\forall i \in X.\\ \end{align} We then define \kappa(\{j\} | i) = K_{ji} = P(j|i), \qquad \forall i \in X, \quad \forall B \in \mathcal B . Again the transition probability, the stochastic matrix and the Markov kernel are equivalent reformulations. \int_Y k(y, x)\nu(\mathrm{d} y) = 1, \qquad \forall x \in X , then \kappa(dy |x) = k(y, x)\nu(dy) i.e. the mapping \begin{cases} \kappa:\mathcal B \times X \to [0,1] \\ \kappa(B|x)=\int_{B}k(y, x)\nu(\mathrm{d} y) \end{cases} defines a Markov kernel. This example generalises the countable Markov process example where \nu was the counting measure. Moreover it encompasses other important examples such as the convolution kernels, in particular the Markov kernels defined by the heat equation. The latter example includes the Gaussian kernel on X = Y = \mathbb R with \nu(dx) = dx standard Lebesgue measure and k_t(y, x) = \frac{1}{\sqrt{2\pi}t}e^{-(y - x)^2/(2t^2)}. Take (X, \mathcal{A}) and (Y, \mathcal{B}) arbitrary measurable spaces, and let f:X \to Y be a measurable function. Now define \kappa(dy|x) = \delta_{f(x)}(dy) i.e. \kappa(B|x) = \mathbf{1}_B(f(x)) = \mathbf{1}_{f^{-1}(B)}(x) = \begin{cases}1 & \text{if } f(x) \in B\\ 0 & \text{otherwise}\end{cases} for all B \in \mathcal{B} . Note that the indicator function \mathbf{1}_{f^{-1}(B)} is \mathcal{A} -measurable for all B \in \mathcal{B} iff f is measurable. This example allows us to think of a Markov kernel as a generalised function with a (in general) random rather than certain value. That is, it is a multivalued function where the values are not equally weighted. ===Galton–Watson process=== As a less obvious example, take X = \N, \mathcal A = \mathcal P(\N) , and (Y, \mathcal B) the real numbers \R with the standard sigma algebra of Borel sets. Then \kappa(B|n)=\begin{cases} \mathbf{1}_B(0) & n=0\\ \Pr(\xi_1 + \cdots + \xi_x \in B) & n \neq 0 \\ \end{cases} where x is the number of element at the state n , \xi_i are i.i.d. random variables (usually with mean 0) and where \mathbf{1}_B is the indicator function. For the simple case of coin flips this models the different levels of a Galton board. == Composition of Markov Kernels== Given measurable spaces (X, \mathcal A) , (Y, \mathcal B) we consider a Markov kernel \kappa: \mathcal B \times X \to [0,1] as a morphism \kappa: X \to Y . Intuitively, rather than assigning to each x \in X a sharply defined point y \in Y the kernel assigns a "fuzzy" point in Y which is only known with some level of uncertainty, much like actual physical measurements. If we have a third measurable space (Z, \mathcal C) , and probability kernels \kappa: X \to Y and \lambda: Y \to Z , we can define a composition \lambda \circ \kappa : X \to Z by the Chapman-Kolmogorov equation (\lambda \circ \kappa) (dz|x) = \int_Y \lambda(dz | y)\kappa(dy|x) . The composition is associative by the Monotone Convergence Theorem and the identity function considered as a Markov kernel (i.e. the delta measure \kappa_{1}(dx'|x) = \delta_x(dx') ) is the unit for this composition. This composition defines the structure of a category on the measurable spaces with Markov kernels as morphisms, first defined by Lawvere, the category of Markov kernels. == Probability Space defined by Probability Distribution and a Markov Kernel== A composition of a probability space (X, \mathcal A, P_X) and a probability kernel \kappa: (X, \mathcal A) \to (Y, \mathcal B) defines a probability space (Y, \mathcal B, P_Y = \kappa \circ P_X) , where the probability measure is given by P_Y(B) = \int_X \int_B \kappa(dy|x) P_X(dx) = \int_X \kappa(B|x)P_X(dx) = \mathbb{E}_{P_X}\kappa(B|\cdot). Q(A \times B) = \int_A \kappa(B|x)\,P(dx), \quad \forall A \in \mathcal A, \quad \forall B \in\mathcal B. P(X \in B\mid\mathcal G)=\mathbb{E} \left [\mathbf 1_{\{X \in B\}}\mid\mathcal G \right ] = \kappa(\cdot,B), \qquad P\text{-a.s.}\,\, \forall B \in Y. It is called regular conditional distribution of X given \mathcal G and is not uniquely defined.

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